## # A tibble: 19 x 2
##      Q29     n
##    <dbl> <int>
##  1    13   889
##  2    NA   735
##  3     5   654
##  4     4   557
##  5     8   347
##  6     3   180
##  7    11   173
##  8    16   151
##  9    10   128
## 10     7   119
## 11    17   111
## 12     1    84
## 13    12    71
## 14    18    69
## 15     6    53
## 16     2    21
## 17    14    13
## 18     9     6
## 19    15     3
## # A tibble: 5 x 2
##     Q32     n
##   <dbl> <int>
## 1     1   963
## 2     2  1063
## 3     3  1068
## 4     4   673
## 5    NA   597
## # A tibble: 5 x 2
##     Q32     n
##   <dbl> <int>
## 1     1    71
## 2     2    75
## 3     3    74
## 4     4    74
## 5    NA   441
## # A tibble: 13 x 2
##      Q33     n
##    <dbl> <int>
##  1     1   925
##  2     2    60
##  3     3   141
##  4     4   854
##  5     5    60
##  6     6    67
##  7     7    65
##  8     8   243
##  9     9   399
## 10    10   393
## 11    11   221
## 12    12   368
## 13    NA   568
## # A tibble: 13 x 2
##      Q33     n
##    <dbl> <int>
##  1     1   869
##  2     4   789
##  3    10   374
##  4     9   367
##  5    12   332
##  6     8   228
##  7    11   196
##  8    NA   130
##  9     3   117
## 10     6    61
## 11     5    57
## 12     7    56
## 13     2    53
## # A tibble: 6 x 2
##     Q37     n
##   <dbl> <int>
## 1     1  2833
## 2     2   954
## 3     3    28
## 4     4    44
## 5  1234     1
## 6    NA   504

Drop NAs for specific questions and filter out disciplines with fewer than 30 (the cutoff) students in sample

## # A tibble: 18 x 2
##      Q29     n
##    <dbl> <int>
##  1    13   622
##  2     5   486
##  3     4   398
##  4     8   241
##  5    16   120
##  6     3   111
##  7    11   111
##  8     7    95
##  9    10    95
## 10    17    81
## 11     1    70
## 12    12    57
## 13    18    42
## 14     6    34
## 15     2    16
## 16    14     8
## 17     9     3
## 18    15     1
## # A tibble: 14 x 2
##      Q29     n
##    <dbl> <int>
##  1    13   622
##  2     5   486
##  3     4   398
##  4     8   241
##  5    16   120
##  6     3   111
##  7    11   111
##  8     7    95
##  9    10    95
## 10    17    81
## 11     1    70
## 12    12    57
## 13    18    42
## 14     6    34

Major counts and percentages

Q29 major n pct_total cumulat_pct
13 Mec 622 24.27 24.27
5 Che 486 18.96 43.23
4 Civ 398 15.53 58.76
8 Ele 241 9.40 68.16
16 Softw 120 4.68 72.84
3 Bio 111 4.33 77.18
11 Ind 111 4.33 81.51
7 Comp 95 3.71 85.21
10 Env/Eco 95 3.71 88.92
17 Str/Arc 81 3.16 92.08
1 Aer/Oce 70 2.73 94.81
12 Mat 57 2.22 97.03
18 Gen 42 1.64 98.67
6 Con 34 1.33 100.00

Gender counts overall

Q37 n pct_total
1 1882 73.43
2 646 25.20
3 14 0.55
4 21 0.82

fill in 0s for NAs for specific items (Q1, Q3, Q5)

Drop majors with low counts (below 30 students in sample)

## # A tibble: 14 x 2
##      Q29     n
##    <dbl> <int>
##  1    13   596
##  2     5   469
##  3     4   390
##  4     8   238
##  5    16   118
##  6     3   110
##  7    11   108
##  8     7    93
##  9    10    90
## 10    17    78
## 11     1    70
## 12    12    55
## 13    18    40
## 14     6    31
## # A tibble: 14 x 2
##    major       n
##    <chr>   <int>
##  1 Mec       596
##  2 Che       469
##  3 Civ       390
##  4 Ele       238
##  5 Softw     118
##  6 Bio       110
##  7 Ind       108
##  8 Comp       93
##  9 Env/Eco    90
## 10 Str/Arc    78
## 11 Aer/Oce    70
## 12 Mat        55
## 13 Gen        40
## 14 Con        31

Drop majors with NA as major

## # A tibble: 14 x 2
##    major       n
##    <chr>   <int>
##  1 Mec       596
##  2 Che       469
##  3 Civ       390
##  4 Ele       238
##  5 Softw     118
##  6 Bio       110
##  7 Ind       108
##  8 Comp       93
##  9 Env/Eco    90
## 10 Str/Arc    78
## 11 Aer/Oce    70
## 12 Mat        55
## 13 Gen        40
## 14 Con        31

Clustering Process (two-step process using UMAP + HDBSCAN)

First perform dimension reduction using UMAP

## NULL
##           [,1]      [,2]
## [1,] -38.02495 -6.968480
## [2,] -31.19541 -3.773391
## [3,]  56.61304  5.437927
## [1] -38.02495 -31.19541  56.61304  56.44075 -18.30809  56.84714
## [1] -6.968480 -3.773391  5.437927  5.263140 22.195229  5.086338

Next, perform clustering with HDBSCAN

## HDBSCAN clustering for 2486 objects.
## Parameters: minPts = 120
## The clustering contains 6 cluster(s) and 103 noise points.
## 
##    0    1    2    3    4    5    6 
##  103 1026  173  549  150  348  137 
## 
## Available fields: cluster, minPts, cluster_scores, membership_prob,
##                   outlier_scores, hc

Join the dataframes back together again

## # A tibble: 7 x 2
##   cluster     n
##     <dbl> <int>
## 1       1  1026
## 2       3   549
## 3       5   348
## 4       2   173
## 5       4   150
## 6       6   137
## 7       0   103

Views of when climate change will affect different groups broken down by cluster assignments

Looking for patterns in the clusters

Understanding cluster compositions more

First create a dataframe with the rankings for each cluster, where a lower ranking means students in the cluster think climate change will affect more categories sooner.

## # A tibble: 7 x 3
##   cluster_time_rank cluster cluster_avg
##               <int>   <dbl>       <dbl>
## 1                 1       1       0.377
## 2                 2       0       3.08 
## 3                 3       4       3.83 
## 4                 4       5       5.14 
## 5                 5       6       7.47 
## 6                 6       3       9.24 
## 7                 7       2      17.4

Set clustering colors for all plots

Look at distribution of cluster for how when they think each community may be affected by global warming

Same information but faceted with clusters

** This is a good plot for seeing that a cluster’s beliefs about effects of global warming on different populations at different times vary in a clear pattern

Overall count for clusters

Broken plot

Q5 career topic interests for all ten topics in Q5 by cluster (instead of major)

Table of average number of topics that students in each cluster identified

## # A tibble: 7 x 3
##   cluster_time_rank     n avg_Q5_total
##               <int> <int>        <dbl>
## 1                 1  1026         3.68
## 2                 2   103         3.22
## 3                 5   137         3.18
## 4                 3   150         3.17
## 5                 4   348         2.97
## 6                 6   549         2.68
## 7                 7   173         2.08

Number of Q5 topics identified in each cluster

Proportions of each cluster interested in Q5 topics

Interests within each cluster

## # A tibble: 10 x 6
## # Groups:   Q5_item [10]
##    Q5_item Q5_item_name          statistic  p.value parameter method            
##    <chr>   <chr>                     <dbl>    <dbl>     <int> <chr>             
##  1 Q5a     Energy (supply/deman~      9.35 1.55e- 1         6 Pearson's Chi-squ~
##  2 Q5b     Disease                    5.14 5.26e- 1         6 Pearson's Chi-squ~
##  3 Q5c     Poverty and wealth d~     52.0  1.88e- 9         6 Pearson's Chi-squ~
##  4 Q5d     Climate change           190.   2.46e-38         6 Pearson's Chi-squ~
##  5 Q5e     Terrorism and war         11.4  7.58e- 2         6 Pearson's Chi-squ~
##  6 Q5f     Water supply              27.9  1.00e- 4         6 Pearson's Chi-squ~
##  7 Q5g     Food availability         28.5  7.41e- 5         6 Pearson's Chi-squ~
##  8 Q5h     Opp. for future gen       12.4  5.41e- 2         6 Pearson's Chi-squ~
##  9 Q5i     Opp. for women and/o~     88.0  7.97e-17         6 Pearson's Chi-squ~
## 10 Q5j     Environmental degrad~    111.   1.44e-21         6 Pearson's Chi-squ~

Chi square tests of whether different clusters (students with different temporal discounting patterns of when climate change will affect various groups) want to pursue various degree options

## # A tibble: 17,402 x 5
##    student_id major cluster_time_rank Q3_item Q3_resp
##         <int> <chr>             <int> <chr>     <dbl>
##  1          1 Ele                   5 Q3a           0
##  2          1 Ele                   5 Q3b           1
##  3          1 Ele                   5 Q3c           0
##  4          1 Ele                   5 Q3d           0
##  5          1 Ele                   5 Q3e           0
##  6          1 Ele                   5 Q3f           0
##  7          1 Ele                   5 Q3g           0
##  8          2 Ele                   4 Q3a           0
##  9          2 Ele                   4 Q3b           1
## 10          2 Ele                   4 Q3c           0
## # ... with 17,392 more rows

Interest of each topic

Interest within each cluster

Chi square tests of degree interests by cluster – not expecting any differences across clusters

## # A tibble: 7 x 6
## # Groups:   Q3_item [7]
##   Q3_item Q3_item_name    statistic p.value parameter method                    
##   <chr>   <chr>               <dbl>   <dbl>     <int> <chr>                     
## 1 Q3a     MA/MS (non-eng)     15.6   0.0164         6 Pearson's Chi-squared test
## 2 Q3b     ME/MS (eng)         15.3   0.0183         6 Pearson's Chi-squared test
## 3 Q3c     PhD (eng)            7.45  0.281          6 Pearson's Chi-squared test
## 4 Q3d     MBA                  5.53  0.478          6 Pearson's Chi-squared test
## 5 Q3e     JD (law)            12.4   0.0545         6 Pearson's Chi-squared test
## 6 Q3f     MD                   3.96  0.682          6 Pearson's Chi-squared test
## 7 Q3g     Other               11.0   0.0869         6 Pearson's Chi-squared test

Kruskal wallis tests of whether different clusters (students with different temporal discounting patterns of when climate change will affect various groups) consider various career satisfaction factors important

Career satisfaction items (Q4 items)

## # A tibble: 39,776 x 5
##    student_id major cluster_time_rank Q4_item Q4_resp
##         <int> <chr>             <int> <chr>     <dbl>
##  1          1 Ele                   5 Q4a           4
##  2          1 Ele                   5 Q4b           3
##  3          1 Ele                   5 Q4c           3
##  4          1 Ele                   5 Q4d           2
##  5          1 Ele                   5 Q4e           4
##  6          1 Ele                   5 Q4f           2
##  7          1 Ele                   5 Q4g           2
##  8          1 Ele                   5 Q4h           4
##  9          1 Ele                   5 Q4i           4
## 10          1 Ele                   5 Q4j           1
## # ... with 39,766 more rows

visualize with boxplots instead of mosaicplots

Table of chi square test results

## # A tibble: 16 x 6
## # Groups:   Q4_item [16]
##    Q4_item Q4_item_name        statistic     p.value parameter method           
##    <chr>   <chr>                   <dbl>       <dbl>     <int> <chr>            
##  1 Q4a     Make money               24.3     4.44e-1        24 Pearson's Chi-sq~
##  2 Q4b     Fame                     25.4     3.82e-1        24 Pearson's Chi-sq~
##  3 Q4c     Help others              56.0     2.32e-4        24 Pearson's Chi-sq~
##  4 Q4d     Supervise others         25.6     3.71e-1        24 Pearson's Chi-sq~
##  5 Q4e     Job sec. and opp.        45.9     4.57e-3        24 Pearson's Chi-sq~
##  6 Q4f     Work w/ people           54.0     4.27e-4        24 Pearson's Chi-sq~
##  7 Q4g     Invent/design            28.2     2.53e-1        24 Pearson's Chi-sq~
##  8 Q4h     Develop knowledge/~      24.3     4.44e-1        24 Pearson's Chi-sq~
##  9 Q4i     Personal/fam. time       31.5     1.40e-1        24 Pearson's Chi-sq~
## 10 Q4j     Easy job                 68.7     3.41e-6        24 Pearson's Chi-sq~
## 11 Q4k     Exciting env.            32.8     1.09e-1        24 Pearson's Chi-sq~
## 12 Q4l     Solve societal pro~      90.5     1.19e-9        24 Pearson's Chi-sq~
## 13 Q4m     Use talent/abiliti~      41.0     1.68e-2        24 Pearson's Chi-sq~
## 14 Q4n     Do hands-on work         25.7     3.69e-1        24 Pearson's Chi-sq~
## 15 Q4o     Apply math/sci.          23.5     4.90e-1        24 Pearson's Chi-sq~
## 16 Q4p     Volunteer w/ chari~      70.6     1.76e-6        24 Pearson's Chi-sq~

Using kruskal-wallis instead of chi square since outcome is Likert-scale item (i.e., ordinal variable)

## # A tibble: 16 x 6
## # Groups:   Q4_item [16]
##    Q4_item Q4_item_name        statistic  p.value parameter method              
##    <chr>   <chr>                   <dbl>    <dbl>     <int> <chr>               
##  1 Q4a     Make money             11.9   1.78e- 2         4 Kruskal-Wallis rank~
##  2 Q4b     Fame                    2.87  5.80e- 1         4 Kruskal-Wallis rank~
##  3 Q4c     Help others            25.8   3.52e- 5         4 Kruskal-Wallis rank~
##  4 Q4d     Supervise others        2.72  6.05e- 1         4 Kruskal-Wallis rank~
##  5 Q4e     Job sec. and opp.      12.0   1.73e- 2         4 Kruskal-Wallis rank~
##  6 Q4f     Work w/ people          3.97  4.11e- 1         4 Kruskal-Wallis rank~
##  7 Q4g     Invent/design           1.30  8.61e- 1         4 Kruskal-Wallis rank~
##  8 Q4h     Develop knowledge/~     9.42  5.14e- 2         4 Kruskal-Wallis rank~
##  9 Q4i     Personal/fam. time      2.69  6.11e- 1         4 Kruskal-Wallis rank~
## 10 Q4j     Easy job                4.58  3.34e- 1         4 Kruskal-Wallis rank~
## 11 Q4k     Exciting env.          14.6   5.63e- 3         4 Kruskal-Wallis rank~
## 12 Q4l     Solve societal pro~    72.0   8.48e-15         4 Kruskal-Wallis rank~
## 13 Q4m     Use talent/abiliti~    19.6   6.07e- 4         4 Kruskal-Wallis rank~
## 14 Q4n     Do hands-on work        4.84  3.04e- 1         4 Kruskal-Wallis rank~
## 15 Q4o     Apply math/sci.         0.555 9.68e- 1         4 Kruskal-Wallis rank~
## 16 Q4p     Volunteer w/ chari~    27.0   2.00e- 5         4 Kruskal-Wallis rank~

Question 2 items (Working in various sectors) and climate change impacts cluster

## # A tibble: 13,762 x 5
##    student_id major cluster_time_rank Q2_item Q2_resp
##         <int> <chr>             <int> <chr>     <dbl>
##  1          1 Ele                   5 Q2a           4
##  2          1 Ele                   5 Q2b           0
##  3          1 Ele                   5 Q2c           4
##  4          1 Ele                   5 Q2d           2
##  5          1 Ele                   5 Q2e           2
##  6          1 Ele                   5 Q2f           2
##  7          1 Ele                   5 Q2g           2
##  8          2 Ele                   4 Q2a           4
##  9          2 Ele                   4 Q2b           1
## 10          2 Ele                   4 Q2c           2
## # ... with 13,752 more rows

Using kruskal-wallis instead of chi square since outcome is Likert-scale item (i.e., ordinal variable)

visualize with boxplots instead of mosaicplots

Table of kruskal wallis test results

## # A tibble: 7 x 6
## # Groups:   Q2_item [7]
##   Q2_item Q2_item_name          statistic  p.value parameter method             
##   <chr>   <chr>                     <dbl>    <dbl>     <int> <chr>              
## 1 Q2a     Private/Corporate          40.9 0.0170          24 Pearson's Chi-squa~
## 2 Q2b     Non-profit/NGO             58.1 0.000117        24 Pearson's Chi-squa~
## 3 Q2c     Gov./Public Policy         20.9 0.647           24 Pearson's Chi-squa~
## 4 Q2d     Education                  39.3 0.0253          24 Pearson's Chi-squa~
## 5 Q2e     Entrepreneurship/Sta~      32.1 0.123           24 Pearson's Chi-squa~
## 6 Q2f     Healthcare                 20.4 0.673           24 Pearson's Chi-squa~
## 7 Q2g     Other                      24.1 0.457           24 Pearson's Chi-squa~

End of career interest section

Cluster beliefs about global warming

The following series of analyses look at differences among the temporal discounting clusters regarding various beliefs about global warming and climate change

Q18k - We should be taking stronger actions to address climate change

##    
##       0   1
##   1 144 861
##   2  25  76
##   3  27 119
##   4  80 263
##   5  41  95
##   6 231 306
##   7 134  36
## 
##  Pearson's Chi-squared test
## 
## data:  cont_table
## X-squared = 382.38, df = 6, p-value < 2.2e-16

Plot for Major by Q20a

## 
##  
##    Cell Contents
## |-------------------------|
## |                       N |
## | Chi-square contribution |
## |           N / Row Total |
## |           N / Col Total |
## |         N / Table Total |
## |-------------------------|
## 
##  
## Total Observations in Table:  2466 
## 
##  
##                | climate_df$Q20a_bin 
## climate_df$Q29 |         0 |         1 | Row Total | 
## ---------------|-----------|-----------|-----------|
##              1 |         6 |        64 |        70 | 
##                |     4.245 |     1.023 |           | 
##                |     0.086 |     0.914 |     0.028 | 
##                |     0.013 |     0.032 |           | 
##                |     0.002 |     0.026 |           | 
## ---------------|-----------|-----------|-----------|
##              3 |        17 |        92 |       109 | 
##                |     0.822 |     0.198 |           | 
##                |     0.156 |     0.844 |     0.044 | 
##                |     0.035 |     0.046 |           | 
##                |     0.007 |     0.037 |           | 
## ---------------|-----------|-----------|-----------|
##              4 |        67 |       319 |       386 | 
##                |     0.849 |     0.205 |           | 
##                |     0.174 |     0.826 |     0.157 | 
##                |     0.140 |     0.161 |           | 
##                |     0.027 |     0.129 |           | 
## ---------------|-----------|-----------|-----------|
##              5 |        83 |       382 |       465 | 
##                |     0.594 |     0.143 |           | 
##                |     0.178 |     0.822 |     0.189 | 
##                |     0.173 |     0.192 |           | 
##                |     0.034 |     0.155 |           | 
## ---------------|-----------|-----------|-----------|
##              6 |        10 |        21 |        31 | 
##                |     2.629 |     0.634 |           | 
##                |     0.323 |     0.677 |     0.013 | 
##                |     0.021 |     0.011 |           | 
##                |     0.004 |     0.009 |           | 
## ---------------|-----------|-----------|-----------|
##              7 |        16 |        76 |        92 | 
##                |     0.196 |     0.047 |           | 
##                |     0.174 |     0.826 |     0.037 | 
##                |     0.033 |     0.038 |           | 
##                |     0.006 |     0.031 |           | 
## ---------------|-----------|-----------|-----------|
##              8 |        60 |       177 |       237 | 
##                |     4.236 |     1.021 |           | 
##                |     0.253 |     0.747 |     0.096 | 
##                |     0.125 |     0.089 |           | 
##                |     0.024 |     0.072 |           | 
## ---------------|-----------|-----------|-----------|
##             10 |         6 |        84 |        90 | 
##                |     7.541 |     1.818 |           | 
##                |     0.067 |     0.933 |     0.036 | 
##                |     0.013 |     0.042 |           | 
##                |     0.002 |     0.034 |           | 
## ---------------|-----------|-----------|-----------|
##             11 |        19 |        87 |       106 | 
##                |     0.123 |     0.030 |           | 
##                |     0.179 |     0.821 |     0.043 | 
##                |     0.040 |     0.044 |           | 
##                |     0.008 |     0.035 |           | 
## ---------------|-----------|-----------|-----------|
##             12 |         8 |        47 |        55 | 
##                |     0.674 |     0.162 |           | 
##                |     0.145 |     0.855 |     0.022 | 
##                |     0.017 |     0.024 |           | 
##                |     0.003 |     0.019 |           | 
## ---------------|-----------|-----------|-----------|
##             13 |       141 |       449 |       590 | 
##                |     6.080 |     1.466 |           | 
##                |     0.239 |     0.761 |     0.239 | 
##                |     0.294 |     0.226 |           | 
##                |     0.057 |     0.182 |           | 
## ---------------|-----------|-----------|-----------|
##             16 |        24 |        93 |       117 | 
##                |     0.071 |     0.017 |           | 
##                |     0.205 |     0.795 |     0.047 | 
##                |     0.050 |     0.047 |           | 
##                |     0.010 |     0.038 |           | 
## ---------------|-----------|-----------|-----------|
##             17 |        15 |        63 |        78 | 
##                |     0.002 |     0.000 |           | 
##                |     0.192 |     0.808 |     0.032 | 
##                |     0.031 |     0.032 |           | 
##                |     0.006 |     0.026 |           | 
## ---------------|-----------|-----------|-----------|
##             18 |         7 |        33 |        40 | 
##                |     0.076 |     0.018 |           | 
##                |     0.175 |     0.825 |     0.016 | 
##                |     0.015 |     0.017 |           | 
##                |     0.003 |     0.013 |           | 
## ---------------|-----------|-----------|-----------|
##   Column Total |       479 |      1987 |      2466 | 
##                |     0.194 |     0.806 |           | 
## ---------------|-----------|-----------|-----------|
## 
##  
## Statistics for All Table Factors
## 
## 
## Pearson's Chi-squared test 
## ------------------------------------------------------------
## Chi^2 =  34.91984     d.f. =  13     p =  0.0008710119 
## 
## 
## 
##    
##       0   1
##   1  77 941
##   2  15  87
##   3  17 132
##   4  42 304
##   5  21 114
##   6 173 373
##   7 134  36
## 
##  Pearson's Chi-squared test
## 
## data:  cont_table
## X-squared = 547.76, df = 6, p-value < 2.2e-16

##          Q5d
## major       0   1
##   Aer/Oce  49  21
##   Bio     100  10
##   Che     286 183
##   Civ     249 141
##   Comp     71  22
##   Con      24   7
##   Ele     176  62
##   Env/Eco  22  68
##   Gen      19  21
##   Ind      83  25
##   Mat      33  22
##   Mec     377 219
##   Softw   101  17
##   Str/Arc  48  30

Plot for Major by Q20b

##    
##       0   1
##   1 168 849
##   2  26  76
##   3  29 120
##   4  80 266
##   5  37  98
##   6 241 305
##   7 144  26
## 
##  Pearson's Chi-squared test
## 
## data:  cont_table
## X-squared = 403.53, df = 6, p-value < 2.2e-16

Plot for Major by Q20d

##    
##       0   1
##   1 312 706
##   2  49  52
##   3  73  76
##   4 151 195
##   5  75  60
##   6 361 185
##   7 154  15
## 
##  Pearson's Chi-squared test
## 
## data:  cont_table
## X-squared = 326.39, df = 6, p-value < 2.2e-16

Q22 - What percentage of climate scientists think that human-caused global warming is happening?

##    
##       0   1
##   1 484 542
##   2  45  58
##   3  63  87
##   4 160 188
##   5  70  67
##   6 357 192
##   7 134  39
## 
##  Pearson's Chi-squared test
## 
## data:  cont_table
## X-squared = 105.29, df = 6, p-value < 2.2e-16

Plot for Q23

Alternative plot for Q23 scores using jitter

Differences in how to slow down climate change by cluster

Initial plot for Q24

Alternative plot for Q24

Q25 Which of the following…

  1. is the most abundant greenhouse gas
  2. amplifies the greenhouse gas effect the most?
  3. should we be most concerned about when thinking about global warming? CO2

Q25a

##    
##       0   1
##   1 867 112
##   2  87  13
##   3 121  21
##   4 282  45
##   5 115  12
##   6 459  63
##   7 137  24
## 
##  Pearson's Chi-squared test
## 
## data:  cont_table_Q25a
## X-squared = 4.1767, df = 6, p-value = 0.6528

Q25b

##    
##       0   1
##   1 582 397
##   2  52  48
##   3  75  67
##   4 193 134
##   5  76  51
##   6 379 143
##   7 121  40
## 
##  Pearson's Chi-squared test
## 
## data:  cont_table_Q25b
## X-squared = 50.155, df = 6, p-value = 4.376e-09

Q25c

##    
##       0   1
##   1 427 552
##   2  36  64
##   3  61  81
##   4 142 185
##   5  56  71
##   6 266 256
##   7 104  57
## 
##  Pearson's Chi-squared test
## 
## data:  cont_table_Q25c
## X-squared = 35.308, df = 6, p-value = 3.756e-06

Q26 - How much do you agree or disagree with the following statements about Earth’s climate?

Recoding Q26 for t/F and then creating a total score out of 8

Initial plot for Q26

Alternative plot for Q26 using geom_jitter

Q28 (global warming as technical or social issue) differences by cluster assignment (boxplots and ANOVA results)

Using geom_jitter

ANOVA for Q27 clusters and Q28 averaged social score

##                                Df Sum Sq Mean Sq F value Pr(>F)    
## as_factor(cluster_time_rank)    6  385.3   64.21   66.95 <2e-16 ***
## Residuals                    2270 2177.0    0.96                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Q28_social_norm ~ as_factor(cluster_time_rank), data = climate_df)
## 
## $`as_factor(cluster_time_rank)`
##            diff        lwr          upr     p adj
## 2-1 -0.26507259 -0.5762095  0.046064330 0.1545843
## 3-1 -0.38805376 -0.6523548 -0.123752687 0.0003088
## 4-1 -0.32761949 -0.5153428 -0.139896153 0.0000059
## 5-1 -0.77319411 -1.0503109 -0.496077358 0.0000000
## 6-1 -0.65960388 -0.8188736 -0.500334155 0.0000000
## 7-1 -1.47044829 -1.7182793 -1.222617258 0.0000000
## 3-2 -0.12298118 -0.5088216  0.262859295 0.9659283
## 4-2 -0.06254690 -0.4005674  0.275473602 0.9981250
## 5-2 -0.50812152 -0.9028512 -0.113391863 0.0028423
## 6-2 -0.39453130 -0.7176174 -0.071445153 0.0059224
## 7-2 -1.20537570 -1.5801263 -0.830625079 0.0000000
## 4-3  0.06043427 -0.2350429  0.355911410 0.9967199
## 5-3 -0.38514035 -0.7441116 -0.026169065 0.0261933
## 6-3 -0.27155012 -0.5498189  0.006718695 0.0611465
## 7-3 -1.08239453 -1.4192727 -0.745516348 0.0000000
## 5-4 -0.44557462 -0.7525687 -0.138580524 0.0003843
## 6-4 -0.33198440 -0.5389108 -0.125058012 0.0000479
## 7-4 -1.14282880 -1.4236702 -0.861987383 0.0000000
## 6-5  0.11359022 -0.1768787  0.404059106 0.9109826
## 7-5 -0.69725418 -1.0442780 -0.350230342 0.0000001
## 7-6 -0.81084440 -1.0735204 -0.548168374 0.0000000

Using geom_jitter instead

ANOVA for Q27 clusters and Q28 averaged technical score

##                                Df Sum Sq Mean Sq F value Pr(>F)    
## as_factor(cluster_time_rank)    6  655.9  109.32   202.8 <2e-16 ***
## Residuals                    2270 1223.5    0.54                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##   Tukey multiple comparisons of means
##     95% family-wise confidence level
## 
## Fit: aov(formula = Q28_tech_norm ~ as_factor(cluster_time_rank), data = climate_df)
## 
## $`as_factor(cluster_time_rank)`
##             diff        lwr         upr     p adj
## 2-1 -0.161184911 -0.3944357  0.07206587 0.3900278
## 3-1 -0.113060952 -0.3112002  0.08507829 0.6269918
## 4-1 -0.118775259 -0.2595063  0.02195576 0.1630589
## 5-1 -0.238820056 -0.4465669 -0.03107326 0.0124850
## 6-1 -0.579112867 -0.6985130 -0.45971274 0.0000000
## 7-1 -2.100046215 -2.2858383 -1.91425412 0.0000000
## 3-2  0.048123960 -0.2411300  0.33737793 0.9989729
## 4-2  0.042409652 -0.2109950  0.29581432 0.9989377
## 5-2 -0.077635145 -0.3735531  0.21828281 0.9873821
## 6-2 -0.417927956 -0.6601367 -0.17571917 0.0000080
## 7-2 -1.938861304 -2.2198015 -1.65792108 0.0000000
## 4-3 -0.005714308 -0.2272254  0.21579677 1.0000000
## 5-3 -0.125759104 -0.3948700  0.14335178 0.8131735
## 6-3 -0.466051916 -0.6746624 -0.25744145 0.0000000
## 7-3 -1.986985264 -2.2395336 -1.73443697 0.0000000
## 5-4 -0.120044797 -0.3501898  0.11010022 0.7209814
## 6-4 -0.460337608 -0.6154646 -0.30521060 0.0000000
## 7-4 -1.981270956 -2.1918100 -1.77073188 0.0000000
## 6-5 -0.340292811 -0.5580493 -0.12253629 0.0000861
## 7-5 -1.861226159 -2.1213804 -1.60107194 0.0000000
## 7-6 -1.520933348 -1.7178543 -1.32401236 0.0000000

End cluster analysis